library(ggplot2)
library(plyr)
library(dplyr)

Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:plyr<U+393C><U+3E32>:

    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union
library(corrplot)
corrplot 0.84 loaded
library(caret)
Loading required package: lattice
library(gridExtra)

Attaching package: <U+393C><U+3E31>gridExtra<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:

    combine
library(scales)
library(Rmisc)
library(ggrepel)
library(psych)

Attaching package: <U+393C><U+3E31>psych<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:scales<U+393C><U+3E32>:

    alpha, rescale

The following objects are masked from <U+393C><U+3E31>package:ggplot2<U+393C><U+3E32>:

    %+%, alpha
                  ################################# Reading in data and combining training and test data ########################################
#read files
train=read.csv("./inputs/train.csv", stringsAsFactors=FALSE)
test=read.csv("./inputs/test.csv", stringsAsFactors=FALSE)
#check read in files
print(train)
print(test)
#remove id from both train and test variables
train=train[,-1]
print(train)
test=test[,-1]
print(test)
#combine train and test sets for analysis and cleaning
#test set has missing saleprice (uneqal dimension for merging with train set)
#make a copy of the test set before adding the extra column
test_tmp=test
test_tmp$SalePrice= NA
print(test_tmp)
#combine the added-column test set with the train set
all=rbind(train,test_tmp)
print(all)
#finding the number of numeric codes
numericVars=which(sapply(all, is.numeric))
numericVarNames=names(numericVars)
sprintf("There are %s numeric variables", length(numericVars))
[1] "There are 37 numeric variables"
                ######################################### Tabulate the number of missing values ####################################################
#tabulate the number of missing values
miss_freq=sort(colSums(is.na(all)),decreasing=TRUE)
print(miss_freq)
       PoolQC   MiscFeature         Alley         Fence     SalePrice   FireplaceQu   LotFrontage 
         2909          2814          2721          2348          1459          1420           486 
  GarageYrBlt  GarageFinish    GarageQual    GarageCond    GarageType      BsmtCond  BsmtExposure 
          159           159           159           159           157            82            82 
     BsmtQual  BsmtFinType2  BsmtFinType1    MasVnrType    MasVnrArea      MSZoning     Utilities 
           81            80            79            24            23             4             2 
 BsmtFullBath  BsmtHalfBath    Functional   Exterior1st   Exterior2nd    BsmtFinSF1    BsmtFinSF2 
            2             2             2             1             1             1             1 
    BsmtUnfSF   TotalBsmtSF    Electrical   KitchenQual    GarageCars    GarageArea      SaleType 
            1             1             1             1             1             1             1 
   MSSubClass       LotArea        Street      LotShape   LandContour     LotConfig     LandSlope 
            0             0             0             0             0             0             0 
 Neighborhood    Condition1    Condition2      BldgType    HouseStyle   OverallQual   OverallCond 
            0             0             0             0             0             0             0 
    YearBuilt  YearRemodAdd     RoofStyle      RoofMatl     ExterQual     ExterCond    Foundation 
            0             0             0             0             0             0             0 
      Heating     HeatingQC    CentralAir     X1stFlrSF     X2ndFlrSF  LowQualFinSF     GrLivArea 
            0             0             0             0             0             0             0 
     FullBath      HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd    Fireplaces    PavedDrive 
            0             0             0             0             0             0             0 
   WoodDeckSF   OpenPorchSF EnclosedPorch    X3SsnPorch   ScreenPorch      PoolArea       MiscVal 
            0             0             0             0             0             0             0 
       MoSold        YrSold SaleCondition 
            0             0             0 
sprintf("There are %s columns of missing values", sum(miss_freq>0))
[1] "There are 35 columns of missing values"
            ######################################### To fix all 34 variables with missing values ################################################

  1. PoolQC: Pool quality

    Ex   Excellent
Gd   Good
TA   Average/Typical
Fa   Fair
NA   No Pool
#replace missing values in PoolQC
#According to the data description, PoolQC has Excellent, Good, Average, Fair and No Pool levels. NA represents No Pool.
all$PoolQC[is.na(all$PoolQC)]='No Pool'
#convert the variable to ordinal class
values=c("No Pool"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$PoolQC=as.integer(revalue(all$PoolQC,values))
The following `from` values were not present in `x`: Po, TA
class(all$PoolQC)
[1] "integer"
#assessing PoolQC with PoolArea
all[all$PoolQC==0 & all$PoolArea>0, c("PoolQC","PoolArea","OverallQual")]
#this indicates some error in filling up of the missing values. '0' indicates no pool yet another variable showing its pool area.
#here we assume pools exist for these houses but without quality ratings.
#we will replace these values with reference to "OverallQual" variable
#there are 10 ordinal categories in "OverallQual" and we resampled it into 5 categories to match with PoolQC range.
all[2421,"PoolQC"]=2
all[2504,"PoolQC"]=3
all[2600,"PoolQC"]=2

  1. MiscFeature: Miscellaneous feature not covered in other categories

    Elev Elevator
Gar2 2nd Garage (if not described in garage section)
Othr Other
Shed Shed (over 100 SF)
TenC Tennis Court
NA   None
#Replace missing values in MiscFeature
#MiscFeature account for the additional facilities that a house might possess, such as elevator, 2nd Gararge, Shed, Tennis Court and Others.
#The NAs are due to the house having no other additional facilities
#replacing 'NA' with 'None' and this feature should bein factor form.
all$MiscFeature[is.na(all$MiscFeature)]='None'
#convert the variable to factor class
all$MiscFeature=as.factor(all$MiscFeature)
class(all$MiscFeature)
[1] "factor"

  1. Alley: Type of alley access to property

    Grvl Gravel
Pave Paved
NA   No alley access
#NA represent no alley access
all$Alley[is.na(all$Alley)]='No alley access'
#convert the variable to factor class
all$Alley=as.factor(all$Alley)
class(all$Alley)
[1] "factor"

  1. Fence: Fence quality

    GdPrv    Good Privacy
MnPrv    Minimum Privacy
GdWo Good Wood
MnWw Minimum Wood/Wire
NA   No Fence
#NA represents No Fence
all$Fence[is.na(all$Fence)]='No Fence'
#converting this to factor class
all$Fence=as.factor(all$Fence)
class(all$Fence)
[1] "factor"

  1. FireplaceQu: Fireplace quality

    Ex   Excellent - Exceptional Masonry Fireplace
Gd   Good - Masonry Fireplace in main level
TA   Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
Fa   Fair - Prefabricated Fireplace in basement
Po   Poor - Ben Franklin Stove
NA   No Fireplace
#replace NA with no Fireplace
all$FireplaceQu[is.na(all$FireplaceQu)]="No Fireplace"
#another variable that is closely related to FireplaceQu is Fireplaces
#and let's check whether the number of houses with no fireplace in "Fireplaces" equates with the count in "FireplaceQu"
sprintf("The number of No Fireplace in variables FireplaceQu and Fireplaces are equal: %s",sum(all$FireplaceQu=="No Fireplace") == sum(all$Fireplaces==0))
[1] "The number of No Fireplace in variables FireplaceQu and Fireplaces are equal: TRUE"
#convert the variable to ordinal class
values=c("No Fireplace"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$FireplaceQu=as.integer(revalue(all$FireplaceQu,values))
class(all$FireplaceQu)
[1] "integer"

  1. LotFrontage: Linear feet of street connected to property

    Neighborhood: Physical locations within Ames city limits

    Blmngtn  Bloomington Heights
Blueste  Bluestem
BrDale   Briardale
BrkSide  Brookside
ClearCr  Clear Creek
CollgCr  College Creek
Crawfor  Crawford
Edwards  Edwards
Gilbert  Gilbert
IDOTRR   Iowa DOT and Rail Road
MeadowV  Meadow Village
Mitchel  Mitchell
Names    North Ames
NoRidge  Northridge
NPkVill  Northpark Villa
NridgHt  Northridge Heights
NWAmes   Northwest Ames
OldTown  Old Town
SWISU    South & West of Iowa State University
Sawyer   Sawyer
SawyerW  Sawyer West
Somerst  Somerset
StoneBr  Stone Brook
Timber   Timberland
Veenker  Veenker
#get the index for the rows with missing values
Lot_NAs=all[is.na(all$LotFrontage),c("Neighborhood","LotFrontage")]
print(Lot_NAs)
idx=as.numeric(rownames(Lot_NAs))
print(class(idx))
[1] "numeric"
print(length(idx))
[1] 486
# #we will use the mean distance of each "Neighbourhood" to replace the missing values in "LotFrontage"
for (i in idx){
      if(is.na(all$LotFrontage[i])){
              all[i,"LotFrontage"]=as.integer(mean(all$LotFrontage[all$Neighborhood==all[i,"Neighborhood"]], na.rm=TRUE))
        }
}
print(all[c(8,13,15,17),"LotFrontage"])
[1] 81 74 75 75
print(class(all$LotFrontage))
[1] "integer"
  1. GarageYrBlt: Year garage was built
#associate houses with missing values in this column as houses that did not rebuild or remodel its garage.
#replace this missing values with the year the house is built.
all[is.na(all$GarageYrBlt),"GarageYrBlt"]=all$YearBuilt[is.na(all$GarageYrBlt)]
class(all$GarageYrBlt)
[1] "integer"
#check whether the values have been replaced
sum(is.na(all$GarageYrBlt))
[1] 0

Putting all variables related to garage together:

Number of missing values in garag-related components:

GarageFinish: Interior finish of the garage = 159 GarageQual: Garage quality = 159 GarageCond: Garage condition = 159 GarageType: Garage location = 157 GarageCars: Size of garage in car capacity = 1 GarageArea: Size of garage in square feet = 1

#GarageFinish, GarageQual, GarageCond, GarageType, GarageCars and GarageArea seems to be inter-related.
tmp=all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & is.na(all$GarageType),]
sprintf("how many of these rows have NA values in GarageFinish, Qual, Cond, and Type: %s", dim(tmp)[1])
[1] "how many of these rows have NA values in GarageFinish, Qual, Cond, and Type: 157"
#the above result indicates the the 157 missing values found in GarageType is also found in GarageFinish, Quala and Cond
#
all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & !is.na(all$GarageType), c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")]
#all[is.na(all$GarageCars) | is.na(all$GarageArea), c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")]
  1. GarageCars: Size of garage in car capacity
  2. GarageArea: Size of garage in square feet
# the record 2127 seems to have a garage as it has value for GarageCars and GarageArea
# on the other hand, 2577 does not seem to have a garage
# lets fix these two entries first
# function: finding mode
getmode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}
# entry 2127
all[2127,"GarageFinish"]=getmode(all$GarageFinish)
all[2127,"GarageQual"]=getmode(all$GarageQual)
all[2127,"GarageCond"]=getmode(all$GarageCond)
# entry 2577
all[2577,"GarageType"]=NA
all[2577,"GarageCars"]=0
all[2577,"GarageArea"]=0
#check the rectification
print(all[c(2127,2577),c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")])
tmp=all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & is.na(all$GarageType),]
sprintf("how many of these rows have NA values in GarageFinish, Qual, Cond, and Type (after replacement): %s", dim(tmp)[1])
[1] "how many of these rows have NA values in GarageFinish, Qual, Cond, and Type (after replacement): 158"

  1. GarageFinish: Interior finish of the garage

    Fin Finished RFn Rough Finished
    Unf Unfinished NA No Garage

#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageFinish),"GarageFinish"]="No Garage"
#check the number of NAs again
sum(is.na(all$GarageFinish))
[1] 0
# convert this variable to ordinal
values=c('No Garage'=0, 'Unf'=1, 'RFn'=2, 'Fin'=3)
all$GarageFinish=as.integer(revalue(all$GarageFinish, values))
class(all$GarageFinish)
[1] "integer"

  1. GarageQual: Garage quality

    Ex Excellent Gd Good TA Typical/Average Fa Fair Po Poor NA No Garage

#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageQual),"GarageQual"]="No Garage"
#check the number of NAs again
sum(is.na(all$GarageQual))
[1] 0
#convert to ordinal
values=c("No Garage"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$GarageQual=as.integer(revalue(all$GarageQual, values))
class(all$GarageQual)
[1] "integer"

  1. GarageCond: Garage condition

    Ex Excellent Gd Good TA Typical/Average Fa Fair Po Poor NA No Garage

#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageCond),"GarageCond"]="No Garage"
#convert to ordinal
values=c("No Garage"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$GarageCond=as.integer(revalue(all$GarageCond, values))
class(all$GarageCond)
[1] "integer"

  1. GarageType: Garage location

    2Types More than one type of garage Attchd Attached to home Basment Basement Garage BuiltIn Built-In (Garage part of house - typically has room above garage) CarPort Car Port Detchd Detached from home NA No Garage

#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageType),"GarageType"]="No Garage"
#convert to factor class
all$GarageType=as.factor(all$GarageType)
class(all$GarageType)
[1] "factor"

Putting all variables related to basement together:

No. of missing values in each variable:

BsmtCond: Evaluates the general condition of the basement = 82 BsmtExposure: Refers to walkout or garden level walls = 82
BsmtQual: Evaluates the height of the basement = 81 BsmtFinType2: Rating of basement finished area = 80 BsmtFinType1: Rating of basement finished area = 79 BsmtFullBath: Basement full bathrooms = 2 BsmtHalfBath: Basement half bathrooms = 2 BsmtFinSF1: Type 1 finished square feet = 1 BsmtFinSF2: Type 2 finished square feet = 1 BsmtUnfSF: Unfinished square feet of basement area = 1 TotalBsmtSF: Total square feet of basement area = 1

#check whether the 79 records with missing values in BsmtFinType1 correspond with those in BsmtCond, Exposure, Qual and FinType2
tmp=all[is.na(all$BsmtFinType1) & is.na(all$BsmtCond) & is.na(all$BsmtExposure) & is.na(all$BsmtQual) & is.na(all$BsmtFinType2),]
print(tmp)
sprintf("There are %s rows with NAs in bsmtfintype1, bsmtcond, bsmtexposure, bsmtqual and bsmtfintype2 concurrently ",dim(tmp)[1])
[1] "There are 79 rows with NAs in bsmtfintype1, bsmtcond, bsmtexposure, bsmtqual and bsmtfintype2 concurrently "
#finding the other records with NA in the basement variables.
all[!is.na(all$BsmtFinType1) & (is.na(all$BsmtCond)|is.na(all$BsmtExposure)|is.na(all$BsmtQual)|is.na(all$BsmtFinType2)),c("BsmtFinType1","BsmtCond","BsmtExposure","BsmtQual","BsmtFinType2")]
#each row is observed to have a missing value out of five variables
#we will replace these NAs with the mode.
all[333,"BsmtFinType2"]=getmode(all$BsmtFinType2)
all[949,"BsmtExposure"]=getmode(all$BsmtExposure)
all[1488,"BsmtExposure"]=getmode(all$BsmtExposure)
all[2041,"BsmtCond"]=getmode(all$BsmtCond)
all[2186,"BsmtCond"]=getmode(all$BsmtCond)
all[2218,"BsmtQual"]=getmode(all$BsmtQual)
all[2219,"BsmtQual"]=getmode(all$BsmtQual)
all[2349,"BsmtExposure"]=getmode(all$BsmtExposure)
all[2525,"BsmtCond"]=getmode(all$BsmtCond)
all[c(333,949,1488,2041,2186,2218,2219,2349,2525),c("BsmtFinType1","BsmtCond","BsmtExposure","BsmtQual","BsmtFinType2")]

  1. BsmtCond: Evaluates the general condition of the basement

    Ex Excellent Gd Good TA Typical - slight dampness allowed Fa Fair - dampness or some cracking or settling Po Poor - Severe cracking, settling, or wetness NA No Basement

#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtCond),"BsmtCond"]="No Basement"
#convert to ordinal
values=c("No Basement"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$BsmtCond=as.integer(revalue(all$BsmtCond, values))
The following `from` values were not present in `x`: Ex
class(all$BsmtCond)
[1] "integer"

  1. BsmtExposure: Refers to walkout or garden level walls

    Gd Good Exposure Av Average Exposure (split levels or foyers typically score average or above)
    Mn Mimimum Exposure No No Exposure NA No Basement

#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtExposure),"BsmtExposure"]="No Basement"
#convert to ordinal
values=c("No Basement"=0,"No"=1,"Mn"=2,"Av"=3,"Gd"=4)
all$BsmtExposure=as.integer(revalue(all$BsmtExposure, values))
class(all$BsmtExposure)
[1] "integer"

  1. BsmtQual: Evaluates the height of the basement

    Ex Excellent (100+ inches) Gd Good (90-99 inches) TA Typical (80-89 inches) Fa Fair (70-79 inches) Po Poor (<70 inches NA No Basement

#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtQual),"BsmtQual"]="No Basement"
#convert to ordinal
values=c("No Basement"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$BsmtQual=as.integer(revalue(all$BsmtQual, values))
The following `from` values were not present in `x`: Po
class(all$BsmtQual)
[1] "integer"

  1. BsmtFinType2: Rating of basement finished area (if multiple types)

    GLQ Good Living Quarters ALQ Average Living Quarters BLQ Below Average Living Quarters
    Rec Average Rec Room LwQ Low Quality Unf Unfinshed NA No Basement

#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtFinType2),"BsmtFinType2"]="No Basement"
#convert to ordinal
values=c("No Basement"=0,"Unf"=1,"LwQ"=2,"Rec"=3,"BLQ"=4,"ALQ"=5,"GLQ"=6)
all$BsmtFinType2=as.integer(revalue(all$BsmtFinType2, values))
class(all$BsmtFinType2)
[1] "integer"

  1. BsmtFinTyp1: Rating of basement finished area

    GLQ Good Living Quarters ALQ Average Living Quarters BLQ Below Average Living Quarters
    Rec Average Rec Room LwQ Low Quality Unf Unfinshed NA No Basement

#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtFinType1),"BsmtFinType1"]="No Basement"
#convert to ordinal
values=c("No Basement"=0,"Unf"=1,"LwQ"=2,"Rec"=3,"BLQ"=4,"ALQ"=5,"GLQ"=6)
all$BsmtFinType1=as.integer(revalue(all$BsmtFinType1, values))
class(all$BsmtFinType1)
[1] "integer"
  1. BsmtFullBath: Basement full bathrooms
#replace NAs with zero
all[is.na(all$BsmtFullBath),"BsmtFullBath"]=0
hist(all$BsmtFullBath, main="Histogram of BsmtFullBath", xlab="number of full bathrooms in basement")

  1. BsmtHalfBath: Basement half bathrooms
#replace NAs with zero
all[is.na(all$BsmtHalfBath),"BsmtHalfBath"]=0
hist(all$BsmtHalfBath, main="Histogram of BsmtHalfBath", xlab="number of half bathrooms in basement")

  1. BsmtFinSF1: Type 1 finished square feet 22.BsmtFinSF2: Type 2 finished square feet
  2. BsmtUnfSF: Unfinished square feet of basement area
  3. TotalBsmtSF: Total square feet of basement area
#replace NAs with zero
#they are integers
all[is.na(all$BsmtFinSF1),"BsmtFinSF1"]=0
all[is.na(all$BsmtFinSF2),"BsmtFinSF2"]=0
all[is.na(all$BsmtUnfSF),"BsmtUnfSF"]=0
all[is.na(all$TotalBsmtSF),"TotalBsmtSF"]=0

Putting related variables, MasVnrType (24 missing values) and MasVnrArea(23 missing values), to assess.

#the number of records with MasVnrType being NA given MasVnrArea is also NA.
tmp=all[is.na(all$MasVnrType) & is.na(all$MasVnrArea),]
sprintf("The number of records with with MasVnrType being NA given MasVnrArea is also NA: %s", dim(tmp)[1])
[1] "The number of records with with MasVnrType being NA given MasVnrArea is also NA: 23"
#finding the extra NA record of MasVnrType
all[is.na(all$MasVnrType) & !is.na(all$MasVnrArea), c("MasVnrType","MasVnrArea")]
#correct entry 2611
table(all$MasVnrType)

 BrkCmn BrkFace    None   Stone 
     25     879    1742     249 
#"BrkFace" has the highest mode after "None"
all[2611,c("MasVnrType")]="BrkFace"
print(all[2611,c("MasVnrType","MasVnrArea")])
#replace NAs with None
all[is.na(all$MasVnrType),"MasVnrType"]="None"

  1. MasVnrType: Masonry veneer type

    BrkCmn Brick Common BrkFace Brick Face CBlock Cinder Block None None Stone Stone

#check the relationship between the different category in MasVnrType and SalePrice
attach(all)
aggregate(all[,c("MasVnrType","SalePrice")],by=list(MasVnrType),FUN=mean,na.rm=TRUE)
argument is not numeric or logical: returning NAargument is not numeric or logical: returning NAargument is not numeric or logical: returning NAargument is not numeric or logical: returning NA
#relationship seems to exist between "MasVnrType" and "SalePrice"
#convert to ordinal
values=c("BrkCmn"=0,"None"=1,"BrkFace"=2,"Stone"=3)
all$MasVnrType=as.integer(revalue(all$MasVnrType, values))
class(all$MasVnrType)
[1] "integer"
  1. MasVnrArea: Masonry veneer area in square feet
#replace NAs with zero
all[is.na(all$MasVnrArea),"MasVnrArea"]=0

  1. MSZoning: Identifies the general zoning classification of the sale.

    A Agriculture C Commercial FV Floating Village Residential I Industrial RH Residential High Density RL Residential Low Density RP Residential Low Density Park RM Residential Medium Density

#input missing values with mode and convert to factor
all[is.na(all$MSZoning),"MSZoning"]=getmode(all$MSZoning)
all$MSZoning=as.factor(all$MSZoning)
class(all$MSZoning)
[1] "factor"

  1. KitchenQual: Kitchen quality

    Ex Excellent Gd Good TA Typical/Average Fa Fair Po Poor

#replace missing values with the mode
all[is.na(all$KitchenQual),"KitchenQual"]=getmode(all$KitchenQual)
sprintf("number of missing values in KitchenQual(after replacement):%s", sum(is.na(all$KitchenQual)))
[1] "number of missing values in KitchenQual(after replacement):0"
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$KitchenQual=as.integer(revalue(all$KitchenQual, values))
The following `from` values were not present in `x`: Po
class(all$KitchenQual)
[1] "integer"

  1. Utilities: Type of utilities available

    AllPub All public Utilities (E,G,W,& S)
    NoSewr Electricity, Gas, and Water (Septic Tank) NoSeWa Electricity and Gas Only ELO Electricity only

#checking the frequency of each level
table(all$Utilities)

AllPub NoSeWa 
  2916      1 
#the table shows that most of the rows are categorised as "AllPub"
#this variable hence serve little value in predicting the response varaiable
all$Utilities=NULL

  1. Functional: Home functionality (Assume typical unless deductions are warranted)

    Typ Typical Functionality Min1 Minor Deductions 1 Min2 Minor Deductions 2 Mod Moderate Deductions Maj1 Major Deductions 1 Maj2 Major Deductions 2 Sev Severely Damaged Sal Salvage only

#replace the missing value with the mode
all[is.na(all$Functional),"Functional"]=getmode(all$Functional)
#convert to ordinal
values=c("Sal"=0,"Sev"=1,"Maj2"=2,"Maj1"=3,"Mod"=4,"Min2"=5,"Min1"=6,"Typ"=7)
all$Functional=as.integer(revalue(all$Functional, values))
The following `from` values were not present in `x`: Sal
class(all$Functional)
[1] "integer"
table(all$Functional)

   1    2    3    4    5    6    7 
   2    9   19   35   70   65 2719 
#checking the distribution of the Functional
table(all$Functional)

   1    2    3    4    5    6    7 
   2    9   19   35   70   65 2719

  1. Exterior1st: Exterior covering on house

    AsbShng Asbestos Shingles AsphShn Asphalt Shingles BrkComm Brick Common BrkFace Brick Face CBlock Cinder Block CemntBd Cement Board HdBoard Hard Board ImStucc Imitation Stucco MetalSd Metal Siding Other Other Plywood Plywood PreCast PreCast Stone Stone Stucco Stucco VinylSd Vinyl Siding Wd Sdng Wood Siding WdShing Wood Shingles

#replace missing values with the mode
all[is.na(all$Exterior1st),"Exterior1st"]=getmode(all$Exterior1st)
#convert as factor
all$Exterior1st= as.factor(all$Exterior1st)
class(all$Exterior1st)
[1] "factor"

  1. Exterior2nd: Exterior covering on house (if more than one material)

    AsbShng Asbestos Shingles AsphShn Asphalt Shingles BrkComm Brick Common BrkFace Brick Face CBlock Cinder Block CemntBd Cement Board HdBoard Hard Board ImStucc Imitation Stucco MetalSd Metal Siding Other Other Plywood Plywood PreCast PreCast Stone Stone Stucco Stucco VinylSd Vinyl Siding Wd Sdng Wood Siding WdShing Wood Shingles

#replace missing values with the mode
all[is.na(all$Exterior2nd),"Exterior2nd"]=getmode(all$Exterior2nd)
#convert as factor
all$Exterior2nd= as.factor(all$Exterior2nd)
class(all$Exterior2nd)
[1] "factor"

33.Electrical: Electrical system

   SBrkr    Standard Circuit Breakers & Romex
   FuseA    Fuse Box over 60 AMP and all Romex wiring (Average) 
   FuseF    60 AMP Fuse Box and mostly Romex wiring (Fair)
   FuseP    60 AMP Fuse Box and mostly knob & tube wiring (poor)
   Mix  Mixed
#replace missing value with mode and convert variable to factor
all[is.na(all$Electrical),"Electrical"]=getmode(all$Electrical)
all$Electrical=as.factor(all$Electrical)
class(all$Electrical)
[1] "factor"
table(all$Electrical)

FuseA FuseF FuseP   Mix SBrkr 
  188    50     8     1  2672

  1. SaleType: Type of sale

    WD Warranty Deed - Conventional CWD Warranty Deed - Cash VWD Warranty Deed - VA Loan New Home just constructed and sold COD Court Officer Deed/Estate Con Contract 15% Down payment regular terms ConLw Contract Low Down payment and low interest ConLI Contract Low Interest ConLD Contract Low Down Oth Other

#replace missing value with mode and convert variable to factor
all[is.na(all$SaleType),"SaleType"]=getmode(all$SaleType)
all$SaleType=as.factor(all$SaleType)
class(all$SaleType)
[1] "factor"
table(all$SaleType)

  COD   Con ConLD ConLI ConLw   CWD   New   Oth    WD 
   87     5    26     9     8    12   239     7  2526 
              ################################### Convert the remaining variables to appropriate class #######################################

  1. Foundation: Type of foundation

    BrkTil   Brick & Tile
CBlock   Cinder Block
PConc    Poured Contrete 
Slab Slab
Stone    Stone
Wood Wood
#converting into factor class
all$Foundation=as.factor(all$Foundation)
class(all$Foundation)
[1] "factor"

  1. Heating: Type of heating

    Floor    Floor Furnace
GasA Gas forced warm air furnace
GasW Gas hot water or steam heat
Grav Gravity furnace 
OthW Hot water or steam heat other than gas
Wall Wall furnace
#converting into factor class
all$Heating=as.factor(all$Heating)
class(all$Heating)
[1] "factor"

  1. HeatingQC: Heating quality and condition

    Ex   Excellent
Gd   Good
TA   Average/Typical
Fa   Fair
Po   Poor
#converting into ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$HeatingQC=as.integer(revalue(all$HeatingQC, values))
class(all$HeatingQC)
[1] "integer"

  1. CentralAir: Central air conditioning

    N    No
Y    Yes
#convert to ordinal
values=c("N"=0,"Y"=1)
all$CentralAir=as.integer(revalue(all$CentralAir, values))
class(all$CentralAir)
[1] "integer"

  1. RoofStyle: Type of roof

    Flat Flat
Gable    Gable
Gambrel  Gabrel (Barn)
Hip  Hip
Mansard  Mansard
Shed Shed
#converting into factor class
all$RoofStyle=as.factor(all$RoofStyle)
class(all$RoofStyle)
[1] "factor"

f.RoofMatl: Roof material

   ClyTile  Clay or Tile
   CompShg  Standard (Composite) Shingle
   Membran  Membrane
   Metal    Metal
   Roll Roll
   Tar&Grv  Gravel & Tar
   WdShake  Wood Shakes
   WdShngl  Wood Shingles
#converting into factor class
all$RoofMatl=as.factor(all$RoofMatl)
class(all$RoofMatl)
[1] "factor"

  1. LandContour: Flatness of the property

    Lvl  Near Flat/Level 
Bnk  Banked - Quick and significant rise from street grade to building
HLS  Hillside - Significant slope from side to side
Low  Depression
#converting into factor class
all$LandContour=as.factor(all$LandContour)
class(all$LandContour)
[1] "factor"

h.LandSlope: Slope of property

   Gtl  Gentle slope
   Mod  Moderate Slope  
   Sev  Severe Slope
#converting to ordinal
values=c("Sev"=0,"Mod"=1,"Gtl"=2)
all$LandSlope=as.integer(revalue(all$LandSlope, values))
class(all$LandSlope)
[1] "integer"

  1. BldgType: Type of dwelling

    1Fam Single-family Detached  
2FmCon   Two-family Conversion; originally built as one-family dwelling
Duplx    Duplex
TwnhsE   Townhouse End Unit
TwnhsI   Townhouse Inside Unit
#convert to factor
all$BldgType=as.factor(all$BldgType)
class(all$BldgType)
[1] "factor"

  1. HouseStyle: Style of dwelling

    1Story   One story
1.5Fin   One and one-half story: 2nd level finished
1.5Unf   One and one-half story: 2nd level unfinished
2Story   Two story
2.5Fin   Two and one-half story: 2nd level finished
2.5Unf   Two and one-half story: 2nd level unfinished
SFoyer   Split Foyer
SLvl Split Level
#convert to factor
all$HouseStyle=as.factor(all$HouseStyle)
class(all$HouseStyle)
[1] "factor"

  1. Neighborhood: Physical locations within Ames city limits

    Blmngtn  Bloomington Heights
Blueste  Bluestem
BrDale   Briardale
BrkSide  Brookside
ClearCr  Clear Creek
CollgCr  College Creek
Crawfor  Crawford
Edwards  Edwards
Gilbert  Gilbert
IDOTRR   Iowa DOT and Rail Road
MeadowV  Meadow Village
Mitchel  Mitchell
Names    North Ames
NoRidge  Northridge
NPkVill  Northpark Villa
NridgHt  Northridge Heights
NWAmes   Northwest Ames
OldTown  Old Town
SWISU    South & West of Iowa State University
Sawyer   Sawyer
SawyerW  Sawyer West
Somerst  Somerset
StoneBr  Stone Brook
Timber   Timberland
Veenker  Veenker
#convert to factor
all$Neighborhood=as.factor(all$Neighborhood)
class(all$Neighborhood)
[1] "factor"

  1. Condition1: Proximity to various conditions

    Artery   Adjacent to arterial street
Feedr    Adjacent to feeder street   
Norm Normal  
RRNn Within 200' of North-South Railroad
RRAn Adjacent to North-South Railroad
PosN Near positive off-site feature--park, greenbelt, etc.
PosA Adjacent to postive off-site feature
RRNe Within 200' of East-West Railroad
RRAe Adjacent to East-West Railroad
#convert to factor
all$Condition1=as.factor(all$Condition1)
class(all$Condition1)
[1] "factor"

  1. Condition2: Proximity to various conditions (if more than one is present)

    Artery   Adjacent to arterial street
Feedr    Adjacent to feeder street   
Norm Normal  
RRNn Within 200' of North-South Railroad
RRAn Adjacent to North-South Railroad
PosN Near positive off-site feature--park, greenbelt, etc.
PosA Adjacent to postive off-site feature
RRNe Within 200' of East-West Railroad
RRAe Adjacent to East-West Railroad
#convert to factor
all$Condition2=as.factor(all$Condition2)
class(all$Condition2)
[1] "factor"

  1. Street: Type of road access to property

    Grvl Gravel  
Pave Paved
#convert to ordinal
values=c("Grvl"=0,"Pave"=1)
all$Street=as.integer(revalue(all$Street, values))
class(all$Street)
[1] "integer"

  1. PavedDrive: Paved driveway

    Y    Paved 
P    Partial Pavement
N    Dirt/Gravel
#convert to ordinal
values=c("N"=0,"P"=1,"Y"=2)
all$PavedDrive=as.integer(revalue(all$PavedDrive, values))
class(all$PavedDrive)
[1] "integer"

  1. ExterQual: Evaluates the quality of the material on the exterior

    Ex   Excellent
Gd   Good
TA   Average/Typical
Fa   Fair
Po   Poor
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$ExterQual=as.integer(revalue(all$ExterQual, values))
The following `from` values were not present in `x`: Po
class(all$ExterQual)
[1] "integer"
#check the distribution of the ordinal variable
table(all$ExterQual)

   1    2    3    4 
  35 1798  979  107

  1. ExterCond: Evaluates the present condition of the material on the exterior

    Ex   Excellent
Gd   Good
TA   Average/Typical
Fa   Fair
Po   Poor
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$ExterCond=as.integer(revalue(all$ExterCond, values))
class(all$ExterCond)
[1] "integer"
#check the distribution of the ordinal variable
table(all$ExterCond)

   0    1    2    3    4 
   3   67 2538  299   12

  1. YrSold: Year Sold (YYYY)

  2. MoSold: Month Sold (MM)

#the prices of houses should be cheaper in earlier years.
#convert YrSold to ordinal
sprintf("Is data type of YrSold integer: %s", is.integer(YrSold))
[1] "Is data type of YrSold integer: TRUE"
#convert to factor
sprintf("Is data type of MoSold integer: %s", is.integer(MoSold))
[1] "Is data type of MoSold integer: TRUE"
all$MoSold=as.factor(all$MoSold)
class(all$MoSold)
[1] "factor"

  1. MSSubClass: Identifies the type of dwelling involved in the sale.

     20  1-STORY 1946 & NEWER ALL STYLES
 30  1-STORY 1945 & OLDER
 40  1-STORY W/FINISHED ATTIC ALL AGES
 45  1-1/2 STORY - UNFINISHED ALL AGES
 50  1-1/2 STORY FINISHED ALL AGES
 60  2-STORY 1946 & NEWER
 70  2-STORY 1945 & OLDER
 75  2-1/2 STORY ALL AGES
 80  SPLIT OR MULTI-LEVEL
 85  SPLIT FOYER
 90  DUPLEX - ALL STYLES AND AGES
120  1-STORY PUD (Planned Unit Development) - 1946 & NEWER
150  1-1/2 STORY PUD - ALL AGES
160  2-STORY PUD - 1946 & NEWER
180  PUD - MULTILEVEL - INCL SPLIT LEV/FOYER
190  2 FAMILY CONVERSION - ALL STYLES AND AGES
#convert to factor
all$MSSubClass=as.factor(all$MSSubClass)
str(all$MSSubClass)
 Factor w/ 16 levels "20","30","40",..: 6 1 6 7 6 5 1 6 5 16 ...

  1. LotShape: General shape of property

    Reg  Regular 
IR1  Slightly irregular
IR2  Moderately Irregular
IR3  Irregular
#convert LotShape as factor
values=c('IR3'=0, 'IR2'=1, 'IR1'=2, 'Reg'=3)
all$LotShape=as.integer(revalue(all$LotShape,values))
class(all$LotShape)
[1] "integer"
#check the distribution of the data
table(all$LotShape)

   0    1    2    3 
  16   76  968 1859

  1. LotConfig: Lot configuration

    Inside   Inside lot
Corner   Corner lot
CulDSac  Cul-de-sac
FR2  Frontage on 2 sides of property
FR3  Frontage on 3 sides of property
#convert LotConfig as factor
all$LotConfig=as.factor(all$LotConfig)
class(all$LotConfig)
[1] "factor"

w.SaleCondition: Condition of sale

   Normal   Normal Sale
   Abnorml  Abnormal Sale -  trade, foreclosure, short sale
   AdjLand  Adjoining Land Purchase
   Alloca   Allocation - two linked properties with separate deeds, typically condo with a garage unit  
   Family   Sale between family members
   Partial  Home was not completed when last assessed (associated with New Homes)
#convert as factor
all$SaleCondition=as.factor(all$SaleCondition)
class(all$SaleCondition)
[1] "factor"
                ################################# Check the number of missing data and structure of the data  #####################################
#tabulate the number of missing values
miss_freq=sort(colSums(is.na(all)),decreasing=TRUE)
print(miss_freq)
    SalePrice    MSSubClass      MSZoning   LotFrontage       LotArea        Street         Alley 
         1459             0             0             0             0             0             0 
     LotShape   LandContour     LotConfig     LandSlope  Neighborhood    Condition1    Condition2 
            0             0             0             0             0             0             0 
     BldgType    HouseStyle   OverallQual   OverallCond     YearBuilt  YearRemodAdd     RoofStyle 
            0             0             0             0             0             0             0 
     RoofMatl   Exterior1st   Exterior2nd    MasVnrType    MasVnrArea     ExterQual     ExterCond 
            0             0             0             0             0             0             0 
   Foundation      BsmtQual      BsmtCond  BsmtExposure  BsmtFinType1    BsmtFinSF1  BsmtFinType2 
            0             0             0             0             0             0             0 
   BsmtFinSF2     BsmtUnfSF   TotalBsmtSF       Heating     HeatingQC    CentralAir    Electrical 
            0             0             0             0             0             0             0 
    X1stFlrSF     X2ndFlrSF  LowQualFinSF     GrLivArea  BsmtFullBath  BsmtHalfBath      FullBath 
            0             0             0             0             0             0             0 
     HalfBath  BedroomAbvGr  KitchenAbvGr   KitchenQual  TotRmsAbvGrd    Functional    Fireplaces 
            0             0             0             0             0             0             0 
  FireplaceQu    GarageType   GarageYrBlt  GarageFinish    GarageCars    GarageArea    GarageQual 
            0             0             0             0             0             0             0 
   GarageCond    PavedDrive    WoodDeckSF   OpenPorchSF EnclosedPorch    X3SsnPorch   ScreenPorch 
            0             0             0             0             0             0             0 
     PoolArea        PoolQC         Fence   MiscFeature       MiscVal        MoSold        YrSold 
            0             0             0             0             0             0             0 
     SaleType SaleCondition 
            0             0 
sprintf("There are %s columns of missing values", sum(miss_freq>0))
[1] "There are 1 columns of missing values"
#check the structure of the data
str(all)
'data.frame':   2919 obs. of  79 variables:
 $ MSSubClass   : Factor w/ 16 levels "20","30","40",..: 6 1 6 7 6 5 1 6 5 16 ...
 $ MSZoning     : Factor w/ 5 levels "C (all)","FV",..: 4 4 4 4 4 4 4 4 5 4 ...
 $ LotFrontage  : int  65 80 68 60 84 85 75 81 51 50 ...
 $ LotArea      : int  8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 ...
 $ Street       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Alley        : Factor w/ 3 levels "Grvl","No alley access",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ LotShape     : int  3 3 2 2 2 2 3 2 3 3 ...
 $ LandContour  : Factor w/ 4 levels "Bnk","HLS","Low",..: 4 4 4 4 4 4 4 4 4 4 ...
 $ LotConfig    : Factor w/ 5 levels "Corner","CulDSac",..: 5 3 5 1 3 5 5 1 5 1 ...
 $ LandSlope    : int  2 2 2 2 2 2 2 2 2 2 ...
 $ Neighborhood : Factor w/ 25 levels "Blmngtn","Blueste",..: 6 25 6 7 14 12 21 17 18 4 ...
 $ Condition1   : Factor w/ 9 levels "Artery","Feedr",..: 3 2 3 3 3 3 3 5 1 1 ...
 $ Condition2   : Factor w/ 8 levels "Artery","Feedr",..: 3 3 3 3 3 3 3 3 3 1 ...
 $ BldgType     : Factor w/ 5 levels "1Fam","2fmCon",..: 1 1 1 1 1 1 1 1 1 2 ...
 $ HouseStyle   : Factor w/ 8 levels "1.5Fin","1.5Unf",..: 6 3 6 6 6 1 3 6 1 2 ...
 $ OverallQual  : int  7 6 7 7 8 5 8 7 7 5 ...
 $ OverallCond  : int  5 8 5 5 5 5 5 6 5 6 ...
 $ YearBuilt    : int  2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 ...
 $ YearRemodAdd : int  2003 1976 2002 1970 2000 1995 2005 1973 1950 1950 ...
 $ RoofStyle    : Factor w/ 6 levels "Flat","Gable",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ RoofMatl     : Factor w/ 8 levels "ClyTile","CompShg",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Exterior1st  : Factor w/ 15 levels "AsbShng","AsphShn",..: 13 9 13 14 13 13 13 7 4 9 ...
 $ Exterior2nd  : Factor w/ 16 levels "AsbShng","AsphShn",..: 14 9 14 16 14 14 14 7 16 9 ...
 $ MasVnrType   : int  2 1 2 1 2 1 3 3 1 1 ...
 $ MasVnrArea   : num  196 0 162 0 350 0 186 240 0 0 ...
 $ ExterQual    : int  3 2 3 2 3 2 3 2 2 2 ...
 $ ExterCond    : int  2 2 2 2 2 2 2 2 2 2 ...
 $ Foundation   : Factor w/ 6 levels "BrkTil","CBlock",..: 3 2 3 1 3 6 3 2 1 1 ...
 $ BsmtQual     : int  4 4 4 3 4 4 5 4 3 3 ...
 $ BsmtCond     : int  3 3 3 4 3 3 3 3 3 3 ...
 $ BsmtExposure : int  1 4 2 1 3 1 3 2 1 1 ...
 $ BsmtFinType1 : int  6 5 6 5 6 6 6 5 1 6 ...
 $ BsmtFinSF1   : num  706 978 486 216 655 ...
 $ BsmtFinType2 : int  1 1 1 1 1 1 1 4 1 1 ...
 $ BsmtFinSF2   : num  0 0 0 0 0 0 0 32 0 0 ...
 $ BsmtUnfSF    : num  150 284 434 540 490 64 317 216 952 140 ...
 $ TotalBsmtSF  : num  856 1262 920 756 1145 ...
 $ Heating      : Factor w/ 6 levels "Floor","GasA",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ HeatingQC    : int  4 4 4 3 4 4 4 4 3 4 ...
 $ CentralAir   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Electrical   : Factor w/ 5 levels "FuseA","FuseF",..: 5 5 5 5 5 5 5 5 2 5 ...
 $ X1stFlrSF    : int  856 1262 920 961 1145 796 1694 1107 1022 1077 ...
 $ X2ndFlrSF    : int  854 0 866 756 1053 566 0 983 752 0 ...
 $ LowQualFinSF : int  0 0 0 0 0 0 0 0 0 0 ...
 $ GrLivArea    : int  1710 1262 1786 1717 2198 1362 1694 2090 1774 1077 ...
 $ BsmtFullBath : num  1 0 1 1 1 1 1 1 0 1 ...
 $ BsmtHalfBath : num  0 1 0 0 0 0 0 0 0 0 ...
 $ FullBath     : int  2 2 2 1 2 1 2 2 2 1 ...
 $ HalfBath     : int  1 0 1 0 1 1 0 1 0 0 ...
 $ BedroomAbvGr : int  3 3 3 3 4 1 3 3 2 2 ...
 $ KitchenAbvGr : int  1 1 1 1 1 1 1 1 2 2 ...
 $ KitchenQual  : int  3 2 3 3 3 2 3 2 2 2 ...
 $ TotRmsAbvGrd : int  8 6 6 7 9 5 7 7 8 5 ...
 $ Functional   : int  7 7 7 7 7 7 7 7 6 7 ...
 $ Fireplaces   : int  0 1 1 1 1 0 1 2 2 2 ...
 $ FireplaceQu  : int  0 3 3 4 3 0 4 3 3 3 ...
 $ GarageType   : Factor w/ 7 levels "2Types","Attchd",..: 2 2 2 6 2 2 2 2 6 2 ...
 $ GarageYrBlt  : int  2003 1976 2001 1998 2000 1993 2004 1973 1931 1939 ...
 $ GarageFinish : int  2 2 2 1 2 1 2 2 1 2 ...
 $ GarageCars   : num  2 2 2 3 3 2 2 2 2 1 ...
 $ GarageArea   : num  548 460 608 642 836 480 636 484 468 205 ...
 $ GarageQual   : int  3 3 3 3 3 3 3 3 2 4 ...
 $ GarageCond   : int  3 3 3 3 3 3 3 3 3 3 ...
 $ PavedDrive   : int  2 2 2 2 2 2 2 2 2 2 ...
 $ WoodDeckSF   : int  0 298 0 0 192 40 255 235 90 0 ...
 $ OpenPorchSF  : int  61 0 42 35 84 30 57 204 0 4 ...
 $ EnclosedPorch: int  0 0 0 272 0 0 0 228 205 0 ...
 $ X3SsnPorch   : int  0 0 0 0 0 320 0 0 0 0 ...
 $ ScreenPorch  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ PoolArea     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ PoolQC       : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Fence        : Factor w/ 5 levels "GdPrv","GdWo",..: 5 5 5 5 5 3 5 5 5 5 ...
 $ MiscFeature  : Factor w/ 5 levels "Gar2","None",..: 2 2 2 2 2 4 2 4 2 2 ...
 $ MiscVal      : int  0 0 0 0 0 700 0 350 0 0 ...
 $ MoSold       : Factor w/ 12 levels "1","2","3","4",..: 2 5 9 2 12 10 8 11 4 1 ...
 $ YrSold       : int  2008 2007 2008 2006 2008 2009 2007 2009 2008 2008 ...
 $ SaleType     : Factor w/ 9 levels "COD","Con","ConLD",..: 9 9 9 9 9 9 9 9 9 9 ...
 $ SaleCondition: Factor w/ 6 levels "Abnorml","AdjLand",..: 5 5 5 1 5 5 5 5 1 5 ...
 $ SalePrice    : int  208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 ...
#save dataframe with its formatting.
saveRDS(all,file="all_DP.rds")
---
title: "Data_Preparation"
output: html_notebook
---

```{r}
library(ggplot2)
library(plyr)
library(dplyr)
library(corrplot)
library(caret)
library(gridExtra)
library(scales)
library(Rmisc)
library(ggrepel)
library(psych)
```


                      ################################# Reading in data and combining training and test data ########################################


```{r}
#read files
train=read.csv("./inputs/train.csv", stringsAsFactors=FALSE)
test=read.csv("./inputs/test.csv", stringsAsFactors=FALSE)

#check read in files
print(train)
print(test)
```

```{r}
#remove id from both train and test variables
train=train[,-1]
print(train)
test=test[,-1]
print(test)
```

```{r}
#combine train and test sets for analysis and cleaning
#test set has missing saleprice (uneqal dimension for merging with train set)
#make a copy of the test set before adding the extra column
test_tmp=test
test_tmp$SalePrice= NA
print(test_tmp)

#combine the added-column test set with the train set
all=rbind(train,test_tmp)
print(all)
```
```{r}
#finding the number of numeric codes
numericVars=which(sapply(all, is.numeric))
numericVarNames=names(numericVars)
sprintf("There are %s numeric variables", length(numericVars))
```



                    ######################################### Tabulate the number of missing values ####################################################


```{r}
#tabulate the number of missing values
miss_freq=sort(colSums(is.na(all)),decreasing=TRUE)
print(miss_freq)
sprintf("There are %s columns of missing values", sum(miss_freq>0))
```


                ######################################### To fix all 34 variables with missing values ################################################


1. PoolQC: Pool quality
		
       Ex	Excellent
       Gd	Good
       TA	Average/Typical
       Fa	Fair
       NA	No Pool

```{r}
#replace missing values in PoolQC
#According to the data description, PoolQC has Excellent, Good, Average, Fair and No Pool levels. NA represents No Pool.
all$PoolQC[is.na(all$PoolQC)]='No Pool'
#convert the variable to ordinal class
values=c("No Pool"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$PoolQC=as.integer(revalue(all$PoolQC,values))
class(all$PoolQC)
```

```{r}
#assessing PoolQC with PoolArea
all[all$PoolQC==0 & all$PoolArea>0, c("PoolQC","PoolArea","OverallQual")]
```

```{r}
#this indicates some error in filling up of the missing values. '0' indicates no pool yet another variable showing its pool area.
#here we assume pools exist for these houses but without quality ratings.
#we will replace these values with reference to "OverallQual" variable
#there are 10 ordinal categories in "OverallQual" and we resampled it into 5 categories to match with PoolQC range.

all[2421,"PoolQC"]=2
all[2504,"PoolQC"]=3
all[2600,"PoolQC"]=2
```

2. MiscFeature: Miscellaneous feature not covered in other categories
		
       Elev	Elevator
       Gar2	2nd Garage (if not described in garage section)
       Othr	Other
       Shed	Shed (over 100 SF)
       TenC	Tennis Court
       NA	None

```{r}
#Replace missing values in MiscFeature
#MiscFeature account for the additional facilities that a house might possess, such as elevator, 2nd Gararge, Shed, Tennis Court and Others.
#The NAs are due to the house having no other additional facilities
#replacing 'NA' with 'None' and this feature should bein factor form.
all$MiscFeature[is.na(all$MiscFeature)]='None'
#convert the variable to factor class
all$MiscFeature=as.factor(all$MiscFeature)
class(all$MiscFeature)
```

3. Alley: Type of alley access to property

       Grvl	Gravel
       Pave	Paved
       NA 	No alley access

```{r}
#NA represent no alley access
all$Alley[is.na(all$Alley)]='No alley access'
#convert the variable to factor class
all$Alley=as.factor(all$Alley)
class(all$Alley)
```

4. Fence: Fence quality
		
       GdPrv	Good Privacy
       MnPrv	Minimum Privacy
       GdWo	Good Wood
       MnWw	Minimum Wood/Wire
       NA	No Fence

```{r}
#NA represents No Fence
all$Fence[is.na(all$Fence)]='No Fence'
#converting this to factor class
all$Fence=as.factor(all$Fence)
class(all$Fence)
```

5. FireplaceQu: Fireplace quality

       Ex	Excellent - Exceptional Masonry Fireplace
       Gd	Good - Masonry Fireplace in main level
       TA	Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
       Fa	Fair - Prefabricated Fireplace in basement
       Po	Poor - Ben Franklin Stove
       NA	No Fireplace
       
```{r}
#replace NA with no Fireplace
all$FireplaceQu[is.na(all$FireplaceQu)]="No Fireplace"

#another variable that is closely related to FireplaceQu is Fireplaces
#and let's check whether the number of houses with no fireplace in "Fireplaces" equates with the count in "FireplaceQu"
sprintf("The number of No Fireplace in variables FireplaceQu and Fireplaces are equal: %s",sum(all$FireplaceQu=="No Fireplace") == sum(all$Fireplaces==0))

#convert the variable to ordinal class
values=c("No Fireplace"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$FireplaceQu=as.integer(revalue(all$FireplaceQu,values))
class(all$FireplaceQu)
```

6. LotFrontage: Linear feet of street connected to property

   Neighborhood: Physical locations within Ames city limits

       Blmngtn	Bloomington Heights
       Blueste	Bluestem
       BrDale	Briardale
       BrkSide	Brookside
       ClearCr	Clear Creek
       CollgCr	College Creek
       Crawfor	Crawford
       Edwards	Edwards
       Gilbert	Gilbert
       IDOTRR	Iowa DOT and Rail Road
       MeadowV	Meadow Village
       Mitchel	Mitchell
       Names	North Ames
       NoRidge	Northridge
       NPkVill	Northpark Villa
       NridgHt	Northridge Heights
       NWAmes	Northwest Ames
       OldTown	Old Town
       SWISU	South & West of Iowa State University
       Sawyer	Sawyer
       SawyerW	Sawyer West
       Somerst	Somerset
       StoneBr	Stone Brook
       Timber	Timberland
       Veenker	Veenker

```{r}
#get the index for the rows with missing values
Lot_NAs=all[is.na(all$LotFrontage),c("Neighborhood","LotFrontage")]
print(Lot_NAs)
idx=as.numeric(rownames(Lot_NAs))
print(class(idx))
print(length(idx))

# #we will use the mean distance of each "Neighbourhood" to replace the missing values in "LotFrontage"
for (i in idx){
      if(is.na(all$LotFrontage[i])){
              all[i,"LotFrontage"]=as.integer(mean(all$LotFrontage[all$Neighborhood==all[i,"Neighborhood"]], na.rm=TRUE))
        }
}
print(all[c(8,13,15,17),"LotFrontage"])
print(class(all$LotFrontage))
```

7. GarageYrBlt: Year garage was built

```{r}
#associate houses with missing values in this column as houses that did not rebuild or remodel its garage.
#replace this missing values with the year the house is built.
all[is.na(all$GarageYrBlt),"GarageYrBlt"]=all$YearBuilt[is.na(all$GarageYrBlt)]
class(all$GarageYrBlt)

#check whether the values have been replaced
sum(is.na(all$GarageYrBlt))
```
Putting all variables related to garage together:

Number of missing values in garag-related components:

GarageFinish: Interior finish of the garage = 159
GarageQual: Garage quality = 159
GarageCond: Garage condition = 159
GarageType: Garage location = 157
GarageCars: Size of garage in car capacity = 1
GarageArea: Size of garage in square feet = 1


```{r}
#GarageFinish, GarageQual, GarageCond, GarageType, GarageCars and GarageArea seems to be inter-related.
tmp=all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & is.na(all$GarageType),]
sprintf("how many of these rows have NA values in GarageFinish, Qual, Cond, and Type: %s", dim(tmp)[1])
```

```{r}
#the above result indicates the the 157 missing values found in GarageType is also found in GarageFinish, Quala and Cond
#
all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & !is.na(all$GarageType), c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")]

#all[is.na(all$GarageCars) | is.na(all$GarageArea), c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")]
```

8. GarageCars: Size of garage in car capacity 
9. GarageArea: Size of garage in square feet 

```{r}
# the record 2127 seems to have a garage as it has value for GarageCars and GarageArea
# on the other hand, 2577 does not seem to have a garage
# lets fix these two entries first

# function: finding mode
getmode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}

# entry 2127
all[2127,"GarageFinish"]=getmode(all$GarageFinish)
all[2127,"GarageQual"]=getmode(all$GarageQual)
all[2127,"GarageCond"]=getmode(all$GarageCond)

# entry 2577
all[2577,"GarageType"]=NA
all[2577,"GarageCars"]=0
all[2577,"GarageArea"]=0

#check the rectification
print(all[c(2127,2577),c("GarageFinish","GarageQual","GarageCond","GarageType","GarageCars","GarageArea")])
```
```{r}
tmp=all[is.na(all$GarageFinish) & is.na(all$GarageQual) & is.na(all$GarageCond) & is.na(all$GarageType),]
sprintf("how many of these rows have NA values in GarageFinish, Qual, Cond, and Type (after replacement): %s", dim(tmp)[1])
```
10. GarageFinish: Interior finish of the garage

       Fin	Finished
       RFn	Rough Finished	
       Unf	Unfinished
       NA	No Garage

```{r}
#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageFinish),"GarageFinish"]="No Garage"

#check the number of NAs again
sum(is.na(all$GarageFinish))

# convert this variable to ordinal
values=c('No Garage'=0, 'Unf'=1, 'RFn'=2, 'Fin'=3)
all$GarageFinish=as.integer(revalue(all$GarageFinish, values))
class(all$GarageFinish)
```
11. GarageQual: Garage quality

       Ex	Excellent
       Gd	Good
       TA	Typical/Average
       Fa	Fair
       Po	Poor
       NA	No Garage

```{r}
#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageQual),"GarageQual"]="No Garage"

#check the number of NAs again
sum(is.na(all$GarageQual))

#convert to ordinal
values=c("No Garage"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$GarageQual=as.integer(revalue(all$GarageQual, values))
class(all$GarageQual)
```
12. GarageCond: Garage condition

       Ex	Excellent
       Gd	Good
       TA	Typical/Average
       Fa	Fair
       Po	Poor
       NA	No Garage

```{r}
#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageCond),"GarageCond"]="No Garage"

#convert to ordinal
values=c("No Garage"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$GarageCond=as.integer(revalue(all$GarageCond, values))
class(all$GarageCond)
```
13. GarageType: Garage location
		
       2Types	More than one type of garage
       Attchd	Attached to home
       Basment	Basement Garage
       BuiltIn	Built-In (Garage part of house - typically has room above garage)
       CarPort	Car Port
       Detchd	Detached from home
       NA	No Garage

```{r}
#the NAs indicate "No Garage"
#replace NAs with "No Garage"
all[is.na(all$GarageType),"GarageType"]="No Garage"

#convert to factor class
all$GarageType=as.factor(all$GarageType)
class(all$GarageType)
```

Putting all variables related to basement together:

No. of missing values in each variable:

BsmtCond: Evaluates the general condition of the basement = 82
BsmtExposure: Refers to walkout or garden level walls = 82  
BsmtQual: Evaluates the height of the basement = 81
BsmtFinType2: Rating of basement finished area = 80
BsmtFinType1: Rating of basement finished area = 79
BsmtFullBath: Basement full bathrooms = 2
BsmtHalfBath: Basement half bathrooms = 2
BsmtFinSF1: Type 1 finished square feet = 1
BsmtFinSF2: Type 2 finished square feet = 1
BsmtUnfSF: Unfinished square feet of basement area = 1
TotalBsmtSF: Total square feet of basement area = 1

```{r}
#check whether the 79 records with missing values in BsmtFinType1 correspond with those in BsmtCond, Exposure, Qual and FinType2
tmp=all[is.na(all$BsmtFinType1) & is.na(all$BsmtCond) & is.na(all$BsmtExposure) & is.na(all$BsmtQual) & is.na(all$BsmtFinType2),]
print(tmp)
sprintf("There are %s rows with NAs in bsmtfintype1, bsmtcond, bsmtexposure, bsmtqual and bsmtfintype2 concurrently ",dim(tmp)[1])

#finding the other records with NA in the basement variables.
all[!is.na(all$BsmtFinType1) & (is.na(all$BsmtCond)|is.na(all$BsmtExposure)|is.na(all$BsmtQual)|is.na(all$BsmtFinType2)),c("BsmtFinType1","BsmtCond","BsmtExposure","BsmtQual","BsmtFinType2")]
```

```{r}
#each row is observed to have a missing value out of five variables
#we will replace these NAs with the mode.

all[333,"BsmtFinType2"]=getmode(all$BsmtFinType2)
all[949,"BsmtExposure"]=getmode(all$BsmtExposure)
all[1488,"BsmtExposure"]=getmode(all$BsmtExposure)
all[2041,"BsmtCond"]=getmode(all$BsmtCond)
all[2186,"BsmtCond"]=getmode(all$BsmtCond)
all[2218,"BsmtQual"]=getmode(all$BsmtQual)
all[2219,"BsmtQual"]=getmode(all$BsmtQual)
all[2349,"BsmtExposure"]=getmode(all$BsmtExposure)
all[2525,"BsmtCond"]=getmode(all$BsmtCond)

all[c(333,949,1488,2041,2186,2218,2219,2349,2525),c("BsmtFinType1","BsmtCond","BsmtExposure","BsmtQual","BsmtFinType2")]
```
14. BsmtCond: Evaluates the general condition of the basement

       Ex	Excellent
       Gd	Good
       TA	Typical - slight dampness allowed
       Fa	Fair - dampness or some cracking or settling
       Po	Poor - Severe cracking, settling, or wetness
       NA	No Basement

```{r}
#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtCond),"BsmtCond"]="No Basement"

#convert to ordinal
values=c("No Basement"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$BsmtCond=as.integer(revalue(all$BsmtCond, values))
class(all$BsmtCond)
```
15. BsmtExposure: Refers to walkout or garden level walls

       Gd	Good Exposure
       Av	Average Exposure (split levels or foyers typically score average or above)	
       Mn	Mimimum Exposure
       No	No Exposure
       NA	No Basement
```{r}
#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtExposure),"BsmtExposure"]="No Basement"

#convert to ordinal
values=c("No Basement"=0,"No"=1,"Mn"=2,"Av"=3,"Gd"=4)
all$BsmtExposure=as.integer(revalue(all$BsmtExposure, values))
class(all$BsmtExposure)
```
16. BsmtQual: Evaluates the height of the basement

       Ex	Excellent (100+ inches)	
       Gd	Good (90-99 inches)
       TA	Typical (80-89 inches)
       Fa	Fair (70-79 inches)
       Po	Poor (<70 inches
       NA	No Basement
       
```{r}
#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtQual),"BsmtQual"]="No Basement"

#convert to ordinal
values=c("No Basement"=0,"Po"=1,"Fa"=2,"TA"=3,"Gd"=4,"Ex"=5)
all$BsmtQual=as.integer(revalue(all$BsmtQual, values))
class(all$BsmtQual)
```
17. BsmtFinType2: Rating of basement finished area (if multiple types)

       GLQ	Good Living Quarters
       ALQ	Average Living Quarters
       BLQ	Below Average Living Quarters	
       Rec	Average Rec Room
       LwQ	Low Quality
       Unf	Unfinshed
       NA	No Basement

```{r}
#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtFinType2),"BsmtFinType2"]="No Basement"

#convert to ordinal
values=c("No Basement"=0,"Unf"=1,"LwQ"=2,"Rec"=3,"BLQ"=4,"ALQ"=5,"GLQ"=6)
all$BsmtFinType2=as.integer(revalue(all$BsmtFinType2, values))
class(all$BsmtFinType2)
```
18. BsmtFinTyp1: Rating of basement finished area

       GLQ	Good Living Quarters
       ALQ	Average Living Quarters
       BLQ	Below Average Living Quarters	
       Rec	Average Rec Room
       LwQ	Low Quality
       Unf	Unfinshed
       NA	No Basement
       
```{r}
#the NAs indicate "No Basement"
#replace NAs with "No Basement"
all[is.na(all$BsmtFinType1),"BsmtFinType1"]="No Basement"

#convert to ordinal
values=c("No Basement"=0,"Unf"=1,"LwQ"=2,"Rec"=3,"BLQ"=4,"ALQ"=5,"GLQ"=6)
all$BsmtFinType1=as.integer(revalue(all$BsmtFinType1, values))
class(all$BsmtFinType1)
```
19. BsmtFullBath: Basement full bathrooms

```{r}
#replace NAs with zero
all[is.na(all$BsmtFullBath),"BsmtFullBath"]=0
hist(all$BsmtFullBath, main="Histogram of BsmtFullBath", xlab="number of full bathrooms in basement")
```
20. BsmtHalfBath: Basement half bathrooms

```{r}
#replace NAs with zero
all[is.na(all$BsmtHalfBath),"BsmtHalfBath"]=0
hist(all$BsmtHalfBath, main="Histogram of BsmtHalfBath", xlab="number of half bathrooms in basement")
```

21. BsmtFinSF1: Type 1 finished square feet
22.BsmtFinSF2: Type 2 finished square feet
23. BsmtUnfSF: Unfinished square feet of basement area
24. TotalBsmtSF: Total square feet of basement area

```{r}
#replace NAs with zero
#they are integers
all[is.na(all$BsmtFinSF1),"BsmtFinSF1"]=0
all[is.na(all$BsmtFinSF2),"BsmtFinSF2"]=0
all[is.na(all$BsmtUnfSF),"BsmtUnfSF"]=0
all[is.na(all$TotalBsmtSF),"TotalBsmtSF"]=0
```

Putting related variables, MasVnrType (24 missing values) and MasVnrArea(23 missing values), to assess.

```{r}
#the number of records with MasVnrType being NA given MasVnrArea is also NA.
tmp=all[is.na(all$MasVnrType) & is.na(all$MasVnrArea),]
sprintf("The number of records with with MasVnrType being NA given MasVnrArea is also NA: %s", dim(tmp)[1])

#finding the extra NA record of MasVnrType
all[is.na(all$MasVnrType) & !is.na(all$MasVnrArea), c("MasVnrType","MasVnrArea")]

#correct entry 2611
table(all$MasVnrType)
#"BrkFace" has the highest mode after "None"
all[2611,c("MasVnrType")]="BrkFace"
print(all[2611,c("MasVnrType","MasVnrArea")])

#replace NAs with None
all[is.na(all$MasVnrType),"MasVnrType"]="None"
```
25. MasVnrType: Masonry veneer type 

       BrkCmn	Brick Common
       BrkFace	Brick Face
       CBlock	Cinder Block
       None	None
       Stone	Stone

```{r}
#check the relationship between the different category in MasVnrType and SalePrice
attach(all)
aggregate(all[,c("MasVnrType","SalePrice")],by=list(MasVnrType),FUN=mean,na.rm=TRUE)
#relationship seems to exist between "MasVnrType" and "SalePrice"
#convert to ordinal
values=c("BrkCmn"=0,"None"=1,"BrkFace"=2,"Stone"=3)
all$MasVnrType=as.integer(revalue(all$MasVnrType, values))
class(all$MasVnrType)
```
26. MasVnrArea: Masonry veneer area in square feet

```{r}
#replace NAs with zero
all[is.na(all$MasVnrArea),"MasVnrArea"]=0
```
27. MSZoning: Identifies the general zoning classification of the sale.
		
       A	Agriculture
       C	Commercial
       FV	Floating Village Residential
       I	Industrial
       RH	Residential High Density
       RL	Residential Low Density
       RP	Residential Low Density Park 
       RM	Residential Medium Density

```{r}
#input missing values with mode and convert to factor
all[is.na(all$MSZoning),"MSZoning"]=getmode(all$MSZoning)
all$MSZoning=as.factor(all$MSZoning)
class(all$MSZoning)
```
28. KitchenQual: Kitchen quality

       Ex	Excellent
       Gd	Good
       TA	Typical/Average
       Fa	Fair
       Po	Poor

```{r}
#replace missing values with the mode
all[is.na(all$KitchenQual),"KitchenQual"]=getmode(all$KitchenQual)
sprintf("number of missing values in KitchenQual(after replacement):%s", sum(is.na(all$KitchenQual)))
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$KitchenQual=as.integer(revalue(all$KitchenQual, values))
class(all$KitchenQual)
```
29. Utilities: Type of utilities available
		
       AllPub	All public Utilities (E,G,W,& S)	
       NoSewr	Electricity, Gas, and Water (Septic Tank)
       NoSeWa	Electricity and Gas Only
       ELO	Electricity only	

```{r}
#checking the frequency of each level
table(all$Utilities)

#the table shows that most of the rows are categorised as "AllPub"
#this variable hence serve little value in predicting the response varaiable
all$Utilities=NULL
```
30. Functional: Home functionality (Assume typical unless deductions are warranted)

       Typ	Typical Functionality
       Min1	Minor Deductions 1
       Min2	Minor Deductions 2
       Mod	Moderate Deductions
       Maj1	Major Deductions 1
       Maj2	Major Deductions 2
       Sev	Severely Damaged
       Sal	Salvage only

```{r}
#replace the missing value with the mode
all[is.na(all$Functional),"Functional"]=getmode(all$Functional)

#convert to ordinal
values=c("Sal"=0,"Sev"=1,"Maj2"=2,"Maj1"=3,"Mod"=4,"Min2"=5,"Min1"=6,"Typ"=7)
all$Functional=as.integer(revalue(all$Functional, values))
class(all$Functional)
table(all$Functional)

#checking the distribution of the Functional
table(all$Functional)
```
31. Exterior1st: Exterior covering on house

       AsbShng	Asbestos Shingles
       AsphShn	Asphalt Shingles
       BrkComm	Brick Common
       BrkFace	Brick Face
       CBlock	Cinder Block
       CemntBd	Cement Board
       HdBoard	Hard Board
       ImStucc	Imitation Stucco
       MetalSd	Metal Siding
       Other	Other
       Plywood	Plywood
       PreCast	PreCast	
       Stone	Stone
       Stucco	Stucco
       VinylSd	Vinyl Siding
       Wd Sdng	Wood Siding
       WdShing	Wood Shingles

```{r}
#replace missing values with the mode
all[is.na(all$Exterior1st),"Exterior1st"]=getmode(all$Exterior1st)

#convert as factor
all$Exterior1st= as.factor(all$Exterior1st)
class(all$Exterior1st)
```
32. Exterior2nd: Exterior covering on house (if more than one material)

       AsbShng	Asbestos Shingles
       AsphShn	Asphalt Shingles
       BrkComm	Brick Common
       BrkFace	Brick Face
       CBlock	Cinder Block
       CemntBd	Cement Board
       HdBoard	Hard Board
       ImStucc	Imitation Stucco
       MetalSd	Metal Siding
       Other	Other
       Plywood	Plywood
       PreCast	PreCast
       Stone	Stone
       Stucco	Stucco
       VinylSd	Vinyl Siding
       Wd Sdng	Wood Siding
       WdShing	Wood Shingles

```{r}
#replace missing values with the mode
all[is.na(all$Exterior2nd),"Exterior2nd"]=getmode(all$Exterior2nd)

#convert as factor
all$Exterior2nd= as.factor(all$Exterior2nd)
class(all$Exterior2nd)
```
33.Electrical: Electrical system

       SBrkr	Standard Circuit Breakers & Romex
       FuseA	Fuse Box over 60 AMP and all Romex wiring (Average)	
       FuseF	60 AMP Fuse Box and mostly Romex wiring (Fair)
       FuseP	60 AMP Fuse Box and mostly knob & tube wiring (poor)
       Mix	Mixed

```{r}
#replace missing value with mode and convert variable to factor
all[is.na(all$Electrical),"Electrical"]=getmode(all$Electrical)

all$Electrical=as.factor(all$Electrical)
class(all$Electrical)
table(all$Electrical)
```
34. SaleType: Type of sale
		
       WD 	Warranty Deed - Conventional
       CWD	Warranty Deed - Cash
       VWD	Warranty Deed - VA Loan
       New	Home just constructed and sold
       COD	Court Officer Deed/Estate
       Con	Contract 15% Down payment regular terms
       ConLw	Contract Low Down payment and low interest
       ConLI	Contract Low Interest
       ConLD	Contract Low Down
       Oth	Other

```{r}
#replace missing value with mode and convert variable to factor
all[is.na(all$SaleType),"SaleType"]=getmode(all$SaleType)

all$SaleType=as.factor(all$SaleType)
class(all$SaleType)
table(all$SaleType)
```

                  ################################### Convert the remaining variables to appropriate class #######################################


a. Foundation: Type of foundation
		
       BrkTil	Brick & Tile
       CBlock	Cinder Block
       PConc	Poured Contrete	
       Slab	Slab
       Stone	Stone
       Wood	Wood


```{r}
#converting into factor class
all$Foundation=as.factor(all$Foundation)
class(all$Foundation)
```
b. Heating: Type of heating
		
       Floor	Floor Furnace
       GasA	Gas forced warm air furnace
       GasW	Gas hot water or steam heat
       Grav	Gravity furnace	
       OthW	Hot water or steam heat other than gas
       Wall	Wall furnace

```{r}
#converting into factor class
all$Heating=as.factor(all$Heating)
class(all$Heating)
```
c. HeatingQC: Heating quality and condition

       Ex	Excellent
       Gd	Good
       TA	Average/Typical
       Fa	Fair
       Po	Poor

```{r}
#converting into ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$HeatingQC=as.integer(revalue(all$HeatingQC, values))
class(all$HeatingQC)
```
d. CentralAir: Central air conditioning

       N	No
       Y	Yes

```{r}
#convert to ordinal
values=c("N"=0,"Y"=1)
all$CentralAir=as.integer(revalue(all$CentralAir, values))
class(all$CentralAir)
```
e. RoofStyle: Type of roof

       Flat	Flat
       Gable	Gable
       Gambrel	Gabrel (Barn)
       Hip	Hip
       Mansard	Mansard
       Shed	Shed

```{r}
#converting into factor class
all$RoofStyle=as.factor(all$RoofStyle)
class(all$RoofStyle)
```
f.RoofMatl: Roof material

       ClyTile	Clay or Tile
       CompShg	Standard (Composite) Shingle
       Membran	Membrane
       Metal	Metal
       Roll	Roll
       Tar&Grv	Gravel & Tar
       WdShake	Wood Shakes
       WdShngl	Wood Shingles

```{r}
#converting into factor class
all$RoofMatl=as.factor(all$RoofMatl)
class(all$RoofMatl)
```
g. LandContour: Flatness of the property

       Lvl	Near Flat/Level	
       Bnk	Banked - Quick and significant rise from street grade to building
       HLS	Hillside - Significant slope from side to side
       Low	Depression

```{r}
#converting into factor class
all$LandContour=as.factor(all$LandContour)
class(all$LandContour)
```
h.LandSlope: Slope of property
		
       Gtl	Gentle slope
       Mod	Moderate Slope	
       Sev	Severe Slope

```{r}
#converting to ordinal
values=c("Sev"=0,"Mod"=1,"Gtl"=2)
all$LandSlope=as.integer(revalue(all$LandSlope, values))
class(all$LandSlope)
```
i. BldgType: Type of dwelling
		
       1Fam	Single-family Detached	
       2FmCon	Two-family Conversion; originally built as one-family dwelling
       Duplx	Duplex
       TwnhsE	Townhouse End Unit
       TwnhsI	Townhouse Inside Unit
       
```{r}
#convert to factor
all$BldgType=as.factor(all$BldgType)
class(all$BldgType)
```
j. HouseStyle: Style of dwelling
	
       1Story	One story
       1.5Fin	One and one-half story: 2nd level finished
       1.5Unf	One and one-half story: 2nd level unfinished
       2Story	Two story
       2.5Fin	Two and one-half story: 2nd level finished
       2.5Unf	Two and one-half story: 2nd level unfinished
       SFoyer	Split Foyer
       SLvl	Split Level

```{r}
#convert to factor
all$HouseStyle=as.factor(all$HouseStyle)
class(all$HouseStyle)
```
k. Neighborhood: Physical locations within Ames city limits

       Blmngtn	Bloomington Heights
       Blueste	Bluestem
       BrDale	Briardale
       BrkSide	Brookside
       ClearCr	Clear Creek
       CollgCr	College Creek
       Crawfor	Crawford
       Edwards	Edwards
       Gilbert	Gilbert
       IDOTRR	Iowa DOT and Rail Road
       MeadowV	Meadow Village
       Mitchel	Mitchell
       Names	North Ames
       NoRidge	Northridge
       NPkVill	Northpark Villa
       NridgHt	Northridge Heights
       NWAmes	Northwest Ames
       OldTown	Old Town
       SWISU	South & West of Iowa State University
       Sawyer	Sawyer
       SawyerW	Sawyer West
       Somerst	Somerset
       StoneBr	Stone Brook
       Timber	Timberland
       Veenker	Veenker

```{r}
#convert to factor
all$Neighborhood=as.factor(all$Neighborhood)
class(all$Neighborhood)
```
l. Condition1: Proximity to various conditions
	
       Artery	Adjacent to arterial street
       Feedr	Adjacent to feeder street	
       Norm	Normal	
       RRNn	Within 200' of North-South Railroad
       RRAn	Adjacent to North-South Railroad
       PosN	Near positive off-site feature--park, greenbelt, etc.
       PosA	Adjacent to postive off-site feature
       RRNe	Within 200' of East-West Railroad
       RRAe	Adjacent to East-West Railroad

```{r}
#convert to factor
all$Condition1=as.factor(all$Condition1)
class(all$Condition1)
```
m. Condition2: Proximity to various conditions (if more than one is present)
		
       Artery	Adjacent to arterial street
       Feedr	Adjacent to feeder street	
       Norm	Normal	
       RRNn	Within 200' of North-South Railroad
       RRAn	Adjacent to North-South Railroad
       PosN	Near positive off-site feature--park, greenbelt, etc.
       PosA	Adjacent to postive off-site feature
       RRNe	Within 200' of East-West Railroad
       RRAe	Adjacent to East-West Railroad

```{r}
#convert to factor
all$Condition2=as.factor(all$Condition2)
class(all$Condition2)
```
n. Street: Type of road access to property

       Grvl	Gravel	
       Pave	Paved
       
```{r}
#convert to ordinal
values=c("Grvl"=0,"Pave"=1)
all$Street=as.integer(revalue(all$Street, values))
class(all$Street)
```
o. PavedDrive: Paved driveway

       Y	Paved 
       P	Partial Pavement
       N	Dirt/Gravel
       
```{r}
#convert to ordinal
values=c("N"=0,"P"=1,"Y"=2)
all$PavedDrive=as.integer(revalue(all$PavedDrive, values))
class(all$PavedDrive)
```
p. ExterQual: Evaluates the quality of the material on the exterior 
		
       Ex	Excellent
       Gd	Good
       TA	Average/Typical
       Fa	Fair
       Po	Poor
       
```{r}
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$ExterQual=as.integer(revalue(all$ExterQual, values))
class(all$ExterQual)
#check the distribution of the ordinal variable
table(all$ExterQual)
```
q. ExterCond: Evaluates the present condition of the material on the exterior
		
       Ex	Excellent
       Gd	Good
       TA	Average/Typical
       Fa	Fair
       Po	Poor
       
```{r}
#convert to ordinal
values=c("Po"=0,"Fa"=1,"TA"=2,"Gd"=3,"Ex"=4)
all$ExterCond=as.integer(revalue(all$ExterCond, values))
class(all$ExterCond)
#check the distribution of the ordinal variable
table(all$ExterCond)
```
r. YrSold: Year Sold (YYYY)

s. MoSold: Month Sold (MM)

```{r}
#the prices of houses should be cheaper in earlier years.
#convert YrSold to ordinal
sprintf("Is data type of YrSold integer: %s", is.integer(YrSold))
#convert to factor
sprintf("Is data type of MoSold integer: %s", is.integer(MoSold))
all$MoSold=as.factor(all$MoSold)
class(all$MoSold)
```
t. MSSubClass: Identifies the type of dwelling involved in the sale.	

        20	1-STORY 1946 & NEWER ALL STYLES
        30	1-STORY 1945 & OLDER
        40	1-STORY W/FINISHED ATTIC ALL AGES
        45	1-1/2 STORY - UNFINISHED ALL AGES
        50	1-1/2 STORY FINISHED ALL AGES
        60	2-STORY 1946 & NEWER
        70	2-STORY 1945 & OLDER
        75	2-1/2 STORY ALL AGES
        80	SPLIT OR MULTI-LEVEL
        85	SPLIT FOYER
        90	DUPLEX - ALL STYLES AND AGES
       120	1-STORY PUD (Planned Unit Development) - 1946 & NEWER
       150	1-1/2 STORY PUD - ALL AGES
       160	2-STORY PUD - 1946 & NEWER
       180	PUD - MULTILEVEL - INCL SPLIT LEV/FOYER
       190	2 FAMILY CONVERSION - ALL STYLES AND AGES

```{r}
#convert to factor
all$MSSubClass=as.factor(all$MSSubClass)
str(all$MSSubClass)
```
u. LotShape: General shape of property

       Reg	Regular	
       IR1	Slightly irregular
       IR2	Moderately Irregular
       IR3	Irregular

```{r}
#convert LotShape as factor
values=c('IR3'=0, 'IR2'=1, 'IR1'=2, 'Reg'=3)
all$LotShape=as.integer(revalue(all$LotShape,values))
class(all$LotShape)
#check the distribution of the data
table(all$LotShape)
```
v. LotConfig: Lot configuration

       Inside	Inside lot
       Corner	Corner lot
       CulDSac	Cul-de-sac
       FR2	Frontage on 2 sides of property
       FR3	Frontage on 3 sides of property

```{r}
#convert LotConfig as factor
all$LotConfig=as.factor(all$LotConfig)
class(all$LotConfig)
```

w.SaleCondition: Condition of sale

       Normal	Normal Sale
       Abnorml	Abnormal Sale -  trade, foreclosure, short sale
       AdjLand	Adjoining Land Purchase
       Alloca	Allocation - two linked properties with separate deeds, typically condo with a garage unit	
       Family	Sale between family members
       Partial	Home was not completed when last assessed (associated with New Homes)

```{r}
#convert as factor
all$SaleCondition=as.factor(all$SaleCondition)
class(all$SaleCondition)
```


                    ################################# Check the number of missing data and structure of the data  #####################################


```{r}
#tabulate the number of missing values
miss_freq=sort(colSums(is.na(all)),decreasing=TRUE)
print(miss_freq)
sprintf("There are %s columns of missing values", sum(miss_freq>0))

#check the structure of the data
str(all)
```

```{r}
#save dataframe with its formatting.
saveRDS(all,file="all_DP.rds")
```

